Abstract

Imaging genetic studies present a particular statistical challenge due to the high dimensionality of each domain's data. Specifically some issues have been raised about the adequate control of false positives in imaging genetics. A previous study assessed the effect of a set of 'null' SNPs on brain structure and function. Using voxelwise tests, they found that the empirical familywise error rate (FWE) was at or below 5%, indicating that false positive risk was controlled. Statistical tests based on the size of significant clusters of contiguous voxels are also widely used, due to their typically increased statistical power over voxel-wise tests. With VBM (voxel-based morphometry) data, an adjustment for non-stationarity is made to account for variations in smoothness across the brain. While non-stationary RFT tests have been validated with simulations in small datasets, they have not yet been evaluated with the large datasets typical to imaging genetics.